llma

__init__.py

@@ -221,7 +221,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json"):
             
             for item in data:
                 x=item["data"]
-                print(apps[0]['filename'] ,item["filename"])
+                # print(apps[0]['filename'] ,item["filename"])
                 if apps[0]['filename']!=item["filename"]:
                     apps.append({
                         "filename":item["filename"],

nodes/Lama.py

@@ -8,14 +8,13 @@ import numpy as np
 import torch
 
 
-os.environ['LAMA_MODEL'] =  os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
-if os.environ.get("LAMA_MODEL"):
-    model_path=os.environ.get("LAMA_MODEL")
-    if not os.path.exists(model_path):
-        os.environ['LAMA_MODEL']=''
-        raise FileNotFoundError(
-            f"lama torchscript model not found: {model_path}"
-                )
+ 
+llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+if not os.path.exists(llma_model_path):
+    os.environ['LAMA_MODEL']=''
+    print(f"lama torchscript model not found: {llma_model_path}")
+else:
+    os.environ['LAMA_MODEL'] = llma_model_path
 
 # Tensor to PIL
 def tensor2pil(image):

web/index.html

@@ -389,9 +389,7 @@
         }
 
         async function get_my_app(filename = null) {
-
             let url = get_url()
-
             const res = await fetch(`${url}/mixlab/workflow`, {
                 method: 'POST',
                 body: JSON.stringify({
@@ -411,10 +409,7 @@
                     })
                 }
             } catch (error) {
-
             }
-
-
             return data
         }
 

web/javascript/ui_mixlab.js

@@ -4,7 +4,6 @@ import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 import { closeIcon } from './svg_icons.js'
 
-
 import {
   GroupNodeConfig,
   GroupNodeHandler
@@ -49,6 +48,38 @@ async function get_nodes_map () {
   return await res.json()
 }
 
+function get_url () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+async function get_my_app (filename = null) {
+  let url = get_url()
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      task: 'my_app',
+      filename
+    })
+  })
+  let result = await res.json()
+  let data = []
+  try {
+    for (const res of result.data) {
+      let { app,workflow } = res.data
+      if (app.filename)
+        data.push({
+          ...app,
+          data: workflow,
+          date: res.date
+        })
+    }
+  } catch (error) {}
+  return data
+}
+
 function loadCSS (url) {
   var link = document.createElement('link')
   link.rel = 'stylesheet'
@@ -668,29 +699,31 @@ app.registerExtension({
         ...options
       ] // and return the options
     }
-    LGraphCanvas.prototype.centerOnNode = function(node) {
-      var dpr = window.devicePixelRatio || 1;  // 获取设备像素比
+    LGraphCanvas.prototype.centerOnNode = function (node) {
+      var dpr = window.devicePixelRatio || 1 // 获取设备像素比
       this.ds.offset[0] =
-          -node.pos[0] -
-          node.size[0] * 0.5 +
-          (this.canvas.width * 0.5) / (this.ds.scale * dpr);  // 考虑设备像素比
+        -node.pos[0] -
+        node.size[0] * 0.5 +
+        (this.canvas.width * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
       this.ds.offset[1] =
-          -node.pos[1] -
-          node.size[1] * 0.5 +
-          (this.canvas.height * 0.5) / (this.ds.scale * dpr);  // 考虑设备像素比
-      this.setDirty(true, true);
-    };
+        -node.pos[1] -
+        node.size[1] * 0.5 +
+        (this.canvas.height * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
+      this.setDirty(true, true)
+    }
   },
   async setup () {
     // Add canvas menu options
     const orig = LGraphCanvas.prototype.getCanvasMenuOptions
 
+    const apps = await get_my_app()
+    // console.log('apps',apps)
     LGraphCanvas.prototype.getCanvasMenuOptions = function () {
       const options = orig.apply(this, arguments)
 
       options.push(null, {
         content: `Nodes Map ♾️Mixlab`,
-        disabled: false, // or a function determining whether to disable
+        disabled: false,
         callback: async () => {
           nodesMap =
             nodesMap && Object.keys(nodesMap).length > 0
@@ -809,7 +842,7 @@ app.registerExtension({
               let d = document.createElement('button')
               d.style = `text-align: left;margin:6px;color: var(--input-text);
                 background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-                d.addEventListener('click', () => {
+              d.addEventListener('click', () => {
                 const node = app.graph.getNodeById(nodeId)
                 if (!node) return
                 app.canvas.centerOnNode(node)
@@ -845,6 +878,27 @@ app.registerExtension({
         }
       })
 
+      options.push({
+        content: 'Workflow App ♾️Mixlab',
+        has_submenu: true,
+        submenu: {
+          options: Array.from(apps, a => {
+            return {
+              content: a.name,
+              callback: async () => {
+                try {
+                  let item = (await get_my_app(a.filename))[0]
+                  if (item) {
+                    console.log(item.data)
+                    app.loadGraphData(item.data)
+                  }
+                } catch (error) {}
+              }
+            }
+          })
+        }
+      })
+
       // options.push({
       //   content: `Save For App ♾️Mixlab`,
       //   disabled: false, // or a function determining whether to disable